Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation

TL;DR

This paper introduces a novel inference pipeline combining parametric lensing models with neural simulation-based inference to constrain warm dark matter properties from strong lensing images, enabling analysis of complex subhalo populations.

Contribution

It presents the first application of truncated marginal neural ratio estimation (TMNRE) to galaxy-galaxy strong lensing data for dark matter mass inference, improving over existing computationally expensive methods.

Findings

TMNRE enables marginalization over large subhalo populations.

The approach can constrain warm dark matter mass in the multi-keV range.

Simulated data analysis demonstrates the method's effectiveness.

Abstract

Precision analysis of galaxy-galaxy strong gravitational lensing images provides a unique way of characterizing small-scale dark matter halos, and could allow us to uncover the fundamental properties of dark matter's constituents. Recently, gravitational imaging techniques made it possible to detect a few heavy subhalos. However, gravitational lenses contain numerous subhalos and line-of-sight halos, whose subtle imprint is extremely difficult to detect individually. Existing methods for marginalizing over this large population of sub-threshold perturbers to infer population-level parameters are typically computationally expensive, or require compressing observations into hand-crafted summary statistics, such as a power spectrum of residuals. Here, we present the first analysis pipeline to combine parametric lensing models and a recently-developed neural simulation-based inference technique called truncated marginal neural ratio estimation (TMNRE) to constrain the warm dark matter halo mass function cutoff scale directly from multiple lensing images. Through a proof-of-concept application to simulated data, we show that our approach enables empirically testable inference of the dark matter cutoff mass through marginalization over a large population of realistic perturbers that would be undetectable on their own, and over lens and source parameters uncertainties. To obtain our results, we combine the signal contained in a set of images with Hubble Space Telescope resolution. Our results suggest that TMNRE can be a powerful approach to put tight constraints on the mass of warm dark matter in the multi-keV regime, which will be relevant both for existing lensing data and in the large sample of lenses that will be delivered by near-future telescopes.